Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development.PP prediction is an essential requirement to ensure safe drilling opera...Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development.PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore stability.However,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex geo-processes.To overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical methods.The empirical method for PP prediction is comprised of data pre-processing and model establishment stage.Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372.Gamma-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction.The normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone data.The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development.The dataset is prepared and divided into two parts:Training and Testing.The PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression model.The Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.展开更多
目的:检测miR-372-3p在膀胱癌组织和转染miR-372-3p mimics膀胱癌细胞中的表达,研究miR-372-3p对膀胱癌5637和T24细胞增殖、迁移与侵袭的影响。方法:采集2016年3月至2017年1月武汉中心医院收治的6例膀胱癌患者癌及癌旁组织(距离肿瘤边缘...目的:检测miR-372-3p在膀胱癌组织和转染miR-372-3p mimics膀胱癌细胞中的表达,研究miR-372-3p对膀胱癌5637和T24细胞增殖、迁移与侵袭的影响。方法:采集2016年3月至2017年1月武汉中心医院收治的6例膀胱癌患者癌及癌旁组织(距离肿瘤边缘>5 cm),q PCR检测膀胱癌及癌旁组织miR-372-3p表达。采用脂质体转染法将miR-372-3p mimics或者miRNC转入膀胱癌5637和T24细胞。用q PCR和Western blotting检测转染miR-372-3p mimics和miR-NC膀胱癌细胞miR-372-3p和ATAD2 m RNA和蛋白E-cadherin、N-cadherin蛋白的表达,流式细胞术检测细胞周期分布,MTT法和集落形成实验检测细胞增殖和集落形成能力,划痕实验和Transwell侵袭实验检测细胞的迁移和侵袭能力。结果:膀胱癌组织miR-372-3p m RNA的表达显著低于癌旁组织(0.65±0.56 vs 1.76±0.34,P<0.01)。和转染miR-NC膀胱癌细胞相比,转染miR-372-3p mimics膀胱癌细胞的miR-372-3p m RNA表达显著增加,ATAD2 m RNA和蛋白的表达显著降低,E-cadherin蛋白表达上调,N-cadherin蛋白表达下调,细胞周期明显阻滞,细胞集落形成和增殖能力显著降低,细胞迁移数和侵袭数减少。结论:miR-372-3p的低表达可能与膀胱癌的发生发展有关,其通过靶向调控ATAD2抑制膀胱癌细胞的增殖、迁移和侵袭能力,可能成为膀胱癌生物治疗的新靶标。展开更多
文摘Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development.PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore stability.However,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex geo-processes.To overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical methods.The empirical method for PP prediction is comprised of data pre-processing and model establishment stage.Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372.Gamma-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction.The normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone data.The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development.The dataset is prepared and divided into two parts:Training and Testing.The PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression model.The Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.
文摘目的:检测miR-372-3p在膀胱癌组织和转染miR-372-3p mimics膀胱癌细胞中的表达,研究miR-372-3p对膀胱癌5637和T24细胞增殖、迁移与侵袭的影响。方法:采集2016年3月至2017年1月武汉中心医院收治的6例膀胱癌患者癌及癌旁组织(距离肿瘤边缘>5 cm),q PCR检测膀胱癌及癌旁组织miR-372-3p表达。采用脂质体转染法将miR-372-3p mimics或者miRNC转入膀胱癌5637和T24细胞。用q PCR和Western blotting检测转染miR-372-3p mimics和miR-NC膀胱癌细胞miR-372-3p和ATAD2 m RNA和蛋白E-cadherin、N-cadherin蛋白的表达,流式细胞术检测细胞周期分布,MTT法和集落形成实验检测细胞增殖和集落形成能力,划痕实验和Transwell侵袭实验检测细胞的迁移和侵袭能力。结果:膀胱癌组织miR-372-3p m RNA的表达显著低于癌旁组织(0.65±0.56 vs 1.76±0.34,P<0.01)。和转染miR-NC膀胱癌细胞相比,转染miR-372-3p mimics膀胱癌细胞的miR-372-3p m RNA表达显著增加,ATAD2 m RNA和蛋白的表达显著降低,E-cadherin蛋白表达上调,N-cadherin蛋白表达下调,细胞周期明显阻滞,细胞集落形成和增殖能力显著降低,细胞迁移数和侵袭数减少。结论:miR-372-3p的低表达可能与膀胱癌的发生发展有关,其通过靶向调控ATAD2抑制膀胱癌细胞的增殖、迁移和侵袭能力,可能成为膀胱癌生物治疗的新靶标。
